Research on Chest X-Ray Image Diagnosis of Covid-19 Based on Improved ResNet

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.

Covid-19 X-Ray Image Detection using ResNet50 and VGG16 in Convolution Neural Network

COVID-19 is an outbreak of disease which is created by China. COVID-19 is originated by coronavirus (CoV), generally created mutation pattern with 'SARS-CoV2' or '2019 novel coronavirus'. It is declared by the World Health Organization of 2019 in December. COVID-19 is a contagious virus and contiguous disease that will create the morality of life. Even though it is detected in an early stage it can be incurable if the severity is more. The throat and nose samples are collected to identify COVID-19 disease. We collected the X-Ray images to identify the virus. We propose a system to diagnose the images using Convolutional Neural Network (CNN) models. Dataset used consists of both Covid and Normal X-ray images. Among Convolutional Neural Network (CNN) models, the proposed models are ResNet50 and VGG16. RESNET50 consists of 48 convolutional, 1 MaxPool, and Average Pool layers, and VGG16 is another convolutional neural network that consists of 16 deep layers. By using these two models, the detection of COVID-19 is done. This research is designed to help physicians for successful detection of COVID-19 disease at an early stage in the medical field. © 2022 IEEE.

Deep-learning models for Covid-19 Detection Using Chest X-Ray Images

Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The problem of COVID-19 detection has become the world's most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients' Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hybrid model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved by the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerable impact on the model's accuracy. Consequently, it is advised that the model be trained with a sufficient number of epochs to provide reliable classification results. The study's findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images. © 2023 IEEE.

Early detection of COVID-19 using deep learning architectures: CNN and resnet-101

The main purpose of this paper was to classify if subject has a COVID-19 or not base on CT scan. CNN and resNet-101 neural network architectures are used to identify the coronavirus. The experimental results showed that the two models CNN and resNet-101 can identify accurately the patients have COVID-19 from others with an excellent accuracy of 83.97 % and 90.05 % respectively. The results demonstrates the best ability of the used models in the current application domain. © 2022 IEEE.

Covid 19 prediction system using CNN

Today, the whole world is fighting the war against Coronavirus. The spread of the virus has been observed in almost all the parts of the world. Covid-19 also known as SARS-Cov-2 was initially observed in China which rapidly multiplied all over the world. The disease is said to spread by cough, normal cold, sneezing or when a person is in close contact with someone who is already infected. Therefore, the spread of the virus can occur when there is direct contact with an infected person or with the objects touched by the infected person. Hence, it is important to detect the contiguous spread of the virus and control it by taking appropriate measures. Several deep learning models have been used in detecting many diseases like Malaria disease, Lung infection, Parkinson's disease etc. Likewise, CNN model along with other transfer techniques is best proven to detect whether a person is infected with covid positive or not. The dataset consists of 1000 images of covid positive and normal x-rays. The proposed model has been trained and tested on the image dataset with the help of transfer learning models in order to improve the performance of the model. The models VGG-16, ResNet-50, Inception v3 and Xception have achieved an overall accuracy of 93%,82%,96% and 92% respectively. The performance of all the 4 architectures are analyzed, understood and hence presented in this paper. It is hence important to classify and detect covid positive infection and contribute towards making the world Covid-free. © 2023 Author(s).

Transfer Learning Model in Deep Neural Network for COVID-19 Prediction from CT Images

Deep learning models are often used to process radi-ological images automatically and can accurately train networks' weights on appropriate datasets. One of the significant benefits of the network is that it is possible to use the weight of a pre-trained network for other applications by fine-tuning the current weight. The primary purpose of this work is to employ a pre-trained deep neural framework known as transfer learning to detect and diagnose COVID-19 in CT images automatically. This paper uses a popular deep neural model, ResNet152, as a neural transfer approach. The presented framework uses the weight obtained from the ImageNet dataset, fine-tuned by the dataset used in the work. The effectiveness of the suggested COVID-19 prediction system is evaluated experimentally and compared with DenseNet, another transfer learning model. The recommended ResNet152 transfer learning model exhibits improved performance and has a 99% accuracy when analogized with the DenseNet201 transfer learning model. © 2022 IEEE.

A new modification CNN using VGG19 and ResNet50V2 for classification of COVID-19 from X-ray radiograph images

Coronavirus often called COVID-19 is a deadly viral disease that causes as a result of severe acute respiratory syndrome coronavirus-2 that needs to be identified especially at its early stages, and failure of which can lead to the further spread of the virus. Despite with the huge success recorded towards the use of the original convolutional neural networks (CNN) of deep learning models. However, their architecture needs to be modified to design their modified versions that can have more powerful feature layer extractors to improve their classification performance. This research is aimed at designing a modified CNN of a deep learning model that can be applied to interpret X-rays to classify COVID-19 cases with improved performance. Therefore, we proposed a modified convolutional neural network (shortened as modification CNN) approach that uses X-rays to classify a COVID-19 case by combining VGG19 and ResNet50V2 along with putting additional dense layers to the combined feature layer extractors. The proposed modified CNN achieved 99.24%, 98.89%, 98.90%, 99.58%, and 99.23% of the overall accuracy, precision, specificity, sensitivity, and F1-Score, respectively. This demonstrates that the results of the proposed approach show a promising classification performance in the classification of COVID-19 cases. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Computerized Face Mask Detection System Using Deep CNN and Transfer Learning

Since the COVID-19 pandemic is on the rise again with hazardous effects in China, it has become very crucial for global individuals and the authorities to avoid spreading of the virus. This research aims to identify algorithms with high accuracy and moderate computing complexity at the same time (although conventional machine learning works on low computation power, we have rather used CNN for our research work as the accuracy of CNN is drastically greater than the former), to identify the proper enforcement of face masks. In order to find the best Neural Network architecture we used many deep CNN Methodologies to solve classification problem in regards of masked and non masked image dataset. In this approach we applied different model architectures, like VGG16, Resnet50, Resnet101 and VGG19, on a large dataset to train on and compared the model on the basis of accuracy in which VGG16 came out to be the best. VGG16 was further tuned with different optimizers to determine the one best fit of the model. VGG16 gave an ideal accuracy of 99.37% with the best fit optimizer over a real life data set. © 2023 IEEE.

An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

Hybrid deep neural network for automatic detection of COVID-19 using chest x-ray images

The 2019 coronavirus (COVID-19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre-trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X-ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre-trained models such as ResNet-50, Inception V3, VGG-16, VGG-19, ResNet50V2, and Xception are used for better feature extraction and Chest X-ray image classification. The overfitting issue were resolved using K-fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques.

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning.

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images.

The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription-Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called "Inception V3 with VGG16 (Visual Geometry Group)" is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.

COVID-19 Detection using adopted convolutional neural networks and high-performance computing.

The COVID 19 pandemic is highly contagious disease is wreaking havoc on people's health and well-being around the world. Radiological imaging with chest radiography is one among the key screening procedure. This disease contaminates the respiratory system and impacts the alveoli, which are small air sacs in the lungs. Several artificial intelligence (AI)-based method to detect COVID-19 have been introduced. The recognition of disease patients using features and variation in chest radiography images was demonstrated using this model. In proposed paper presents a model, a deep convolutional neural network (CNN) with ResNet50 configuration, that really is freely-available and accessible to the common people for detecting this infection from chest radiography scans. The introduced model is capable of recognizing coronavirus diseases from CT scan images that identifies the real time condition of covid-19 patients. Furthermore, the database is capable of tracking detected patients and maintaining their database for increasing accuracy of the training model. The proposed model gives approximately 97% accuracy in determining the above-mentioned results related to covid-19 disease by employing the combination of adopted-CNN and ResNet50 algorithms.

Future forecasting prediction of Covid-19 using hybrid deep learning algorithm.

Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

Sustainable strategy for online physical education teaching using ResNet34 and big data.

Since the global COVID-19 outbreak in the spring of 2020, online instruction has replaced traditional classroom instruction as the main method of educating students. Teaching physical education online can be challenging, as it may be difficult to teach students certain movements, accurate student mobility, and appropriate exercise assignments. This paper proposed an online teaching support system with sustainable development features that utilize several large data sets. The system is based on the deep learning image recognition algorithm ResNet34, which can analyze and correct student actions in real-time for gymnastics, dance, basketball, and other sports. By combining the attention mechanism module with the original ResNet34, the detection precision of the system can be enhanced. The sustainability of the system is evident from the fact that the data set can be expanded in response to the emergence of new sports categories and can be kept current in real-time. According to experiments, the target identification accuracy of the proposed system, which combines ResNet34 and the attention mechanism, is higher than that of several other methods currently in use. The proposed techniques outperform the original ResNet34 in terms of accuracy, precision, and recall by 4.1%, 2.8%, and 3.6%, respectively. The suggested approach significantly improves student action correction in virtual sports instruction.

Performance Evaluation of Learning Models for the Prognosis of COVID-19.

COVID-19 has developed as a worldwide pandemic that needs ways to be detected. It is a communicable disease and is spreading widely. Deep learning and transfer learning methods have achieved promising results and performance for the detection of COVID-19. Therefore, a hybrid deep transfer learning technique has been proposed in this study to detect COVID-19 from chest X-ray images. The work done previously contains a very less number of COVID-19 X-ray images. However, the dataset taken in this work is balanced with a total of 28,384 X-ray images, having 14,192 images in the COVID-19 class and 14,192 images in the normal class. Experimental evaluations were conducted using a chest X-ray dataset to test the efficacy of the proposed hybrid technique. The results clearly reveal that the proposed hybrid technique attains better performance in comparison to the existing contemporary transfer learning and deep learning techniques.

Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

BACKGROUND AND MOTIVATION: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.

Segmentation of COVID-19 Chest CT Images Based on SwishUnet

Coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has been spreading since 2019 until now. Chest CT-scan images have contributed significantly to the prognosis, diagnosis, and detection of complications in COVID-19. Automatic segmentation of COVID-19 infections involving ground-glass opacities and consolidation can assist radiologists in COVID-19 screening, which helps reduce time spent analyzing the infection. In this study, we proposed a novel deep learning network to segment lung damage caused by COVID-19 by utilizing EfficientNet and Resnet as the encoder and a modified U-Net with Swish activation, namely swishUnet, as the decoder. In particular, swishUnet allows the model to deal with smoothness, non-monotonicity, and one-sided boundedness at zero. Three experiments were conducted to evaluate the performance of the proposed architecture on the 100 CT scans and 9 volume CT scans from Italian the society of medical and interventional radiology. The results of the first experiment showed that the best sensitivity was 82.7% using the Resnet+swishUnet method with the Tversky loss function. In the second experiment, the architecture with basic Unet only got a sensitivity of 67.2. But with our proposed method, we can improve to 88.1% by using EfficientNet+SwishUnet. For the third experiment, the best performance sensitivity is Resnet+swishUnet with 79.8%. All models with SwishUnet have the same specificity where the value is 99.8%. From the experiments we conclude that our proposed method with SwishUnet encoder has better performance than the previous method © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.

An empirical analysis of deep ensemble approach on COVID-19 and tuberculosis X-ray images

COVID-19 is a pandemic and a highly contagious disease that can severely damage the respiratory organs. Tuberculosis is also one of the leading respiratory diseases that affect public health. While COVID-19 has pushed the world into chaos and tuberculosis is still a persistent problem in many countries. A chest X-ray can provide plethora of information regarding the type of disease and the extent of damage to the lungs. Since X-rays are widely accessible and can be used in the diagnosis of COVID-19 or tuberculosis, this study aims to leverage those property to classify them in the category of COVID-19 infected lungs, tuberculosis infected lungs or normal lungs. In this paper, an ensemble deep learning model consisting of pre-trained models for feature extraction is used along with machine learning classifiers to classify the X-ray images. Various ensemble models were implemented and highest achieved accuracy among them was observed as 93%.